c691bd381a
Step 4 of β migration: 3 SP14/SP13-EGF producers fire per-rollout-step
in collector using collector-owned kernel handles + collector stream.
Reads rollout-time q_values (post-expected-Q, pre-IQR/ensemble/noise)
+ exp_aux_nb_softmax. Writes to shared ISV.
Producer order preserved (matches trainer submit_aux_ops chain):
1. SP13 dir-acc reduce → 2 fixed-α EMAs → aux_pred to ISV[375]
2. SP14 q_disagreement_update (reads aux softmax + q_values)
3. SP14 alpha_grad_compute (pure ISV state machine)
Same kernel gate (commit 9d0c124ce) preserves EMAs across
no-contribution rollout steps.
q_logits semantic note: collector's q_values buffer is the
post-expected-Q output, BEFORE IQR/ensemble/noise SAXPY bonuses (those
run after this block). The kernel's argmax-over-K=4 finds Q's intended
direction; this matches the trainer's q_out_buf semantic exactly. If a
future audit shows noise-induced argmax flips matter, the launch site
is one indirection from the noise-free expected_q_kernel output.
Gated on isv_signals_dev_ptr != 0 && trainer_params_ptr != 0. No
seed_phase_active_cache gate — EGF Gate 1 needs to observe both
seed-phase scripted-policy and post-seed Q-policy actions across the
curriculum.
Compile clean; sp14_oracle_tests 2/2 non-GPU pass (7 GPU tests
ignored on RTX 3050 Ti host).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ml
10-model ML ensemble for the Foxhunt HFT system, built on Candle v0.9.1.
Models
- DQN (Rainbow) — deep Q-network with prioritized replay, dueling heads, noisy nets
- PPO — proximal policy optimization with GAE, LSTM policies, clip-higher
- TFT — temporal fusion transformer for multi-horizon forecasting
- Mamba2 — state space model for sequence prediction
- Liquid Networks — biologically inspired networks for non-stationary data
- TLOB — transformer-based limit order book analysis
- KAN — Kolmogorov-Arnold networks
- xLSTM — extended LSTM architecture
- TGGN — temporal graph neural network
- Diffusion — diffusion-based generative model
Key Modules
ensemble— model ensemble coordination and confidence aggregationhyperopt— PSO-based hyperparameter optimization with per-model adapterstrainers— unified training loops (DQN, PPO, supervised)inference—InferenceAdaptertrait for predictioncheckpoint— model checkpointing and restorationevaluation— walk-forward evaluation pipeline
Usage
use ml::dqn::DQN;
use ml::ppo::PpoTrainer;